827 research outputs found
Joint source-channel coding with feedback
This paper quantifies the fundamental limits of variable-length transmission
of a general (possibly analog) source over a memoryless channel with noiseless
feedback, under a distortion constraint. We consider excess distortion, average
distortion and guaranteed distortion (-semifaithful codes). In contrast to
the asymptotic fundamental limit, a general conclusion is that allowing
variable-length codes and feedback leads to a sizable improvement in the
fundamental delay-distortion tradeoff. In addition, we investigate the minimum
energy required to reproduce source samples with a given fidelity after
transmission over a memoryless Gaussian channel, and we show that the required
minimum energy is reduced with feedback and an average (rather than maximal)
power constraint.Comment: To appear in IEEE Transactions on Information Theor
Energy Harvesting Wireless Communications: A Review of Recent Advances
This article summarizes recent contributions in the broad area of energy
harvesting wireless communications. In particular, we provide the current state
of the art for wireless networks composed of energy harvesting nodes, starting
from the information-theoretic performance limits to transmission scheduling
policies and resource allocation, medium access and networking issues. The
emerging related area of energy transfer for self-sustaining energy harvesting
wireless networks is considered in detail covering both energy cooperation
aspects and simultaneous energy and information transfer. Various potential
models with energy harvesting nodes at different network scales are reviewed as
well as models for energy consumption at the nodes.Comment: To appear in the IEEE Journal of Selected Areas in Communications
(Special Issue: Wireless Communications Powered by Energy Harvesting and
Wireless Energy Transfer
Approximate Decoding Approaches for Network Coded Correlated Data
This paper considers a framework where data from correlated sources are
transmitted with help of network coding in ad-hoc network topologies. The
correlated data are encoded independently at sensors and network coding is
employed in the intermediate nodes in order to improve the data delivery
performance. In such settings, we focus on the problem of reconstructing the
sources at decoder when perfect decoding is not possible due to losses or
bandwidth bottlenecks. We first show that the source data similarity can be
used at decoder to permit decoding based on a novel and simple approximate
decoding scheme. We analyze the influence of the network coding parameters and
in particular the size of finite coding fields on the decoding performance. We
further determine the optimal field size that maximizes the expected decoding
performance as a trade-off between information loss incurred by limiting the
resolution of the source data and the error probability in the reconstructed
data. Moreover, we show that the performance of the approximate decoding
improves when the accuracy of the source model increases even with simple
approximate decoding techniques. We provide illustrative examples about the
possible of our algorithms that can be deployed in sensor networks and
distributed imaging applications. In both cases, the experimental results
confirm the validity of our analysis and demonstrate the benefits of our low
complexity solution for delivery of correlated data sources
The role of Signal Processing in Meeting Privacy Challenges [an overview]
International audienceWith the increasing growth and sophistication of information technology, personal information is easily accessible electronically. This flood of released personal data raises important privacy concerns. However, electronic data sources exist to be used and have tremendous value (utility) to their users and collectors, leading to a tension between privacy and utility. This article aims to quantify that tension by means of an information-theoretic framework and motivate signal processing approaches to privacy problems. The framework is applied to a number of case studies to illustrate concretely how signal processing can be harnessed to provide data privacy
Asymptotic Task-Based Quantization with Application to Massive MIMO
Quantizers take part in nearly every digital signal processing system which
operates on physical signals. They are commonly designed to accurately
represent the underlying signal, regardless of the specific task to be
performed on the quantized data. In systems working with high-dimensional
signals, such as massive multiple-input multiple-output (MIMO) systems, it is
beneficial to utilize low-resolution quantizers, due to cost, power, and memory
constraints. In this work we study quantization of high-dimensional inputs,
aiming at improving performance under resolution constraints by accounting for
the system task in the quantizers design. We focus on the task of recovering a
desired signal statistically related to the high-dimensional input, and analyze
two quantization approaches: We first consider vector quantization, which is
typically computationally infeasible, and characterize the optimal performance
achievable with this approach. Next, we focus on practical systems which
utilize hardware-limited scalar uniform analog-to-digital converters (ADCs),
and design a task-based quantizer under this model. The resulting system
accounts for the task by linearly combining the observed signal into a lower
dimension prior to quantization. We then apply our proposed technique to
channel estimation in massive MIMO networks. Our results demonstrate that a
system utilizing low-resolution scalar ADCs can approach the optimal channel
estimation performance by properly accounting for the task in the system
design
Modulation and Estimation with a Helper
The problem of transmitting a parameter value over an additive white Gaussian
noise (AWGN) channel is considered, where, in addition to the transmitter and
the receiver, there is a helper that observes the noise non-causally and
provides a description of limited rate to the transmitter and/or
the receiver. We derive upper and lower bounds on the optimal achievable
-th moment of the estimation error and show that they coincide for
small values of and for low SNR values. The upper bound relies on a
recently proposed channel-coding scheme that effectively conveys
bits essentially error-free and the rest of the rate - over the same AWGN
channel without help, with the error-free bits allocated to the most
significant bits of the quantized parameter. We then concentrate on the setting
with a total transmit energy constraint, for which we derive achievability
results for both channel coding and parameter modulation for several scenarios:
when the helper assists only the transmitter or only the receiver and knows the
noise, and when the helper assists the transmitter and/or the receiver and
knows both the noise and the message. In particular, for the message-informed
helper that assists both the receiver and the transmitter, it is shown that the
error probability in the channel-coding task decays doubly exponentially.
Finally, we translate these results to those for continuous-time power-limited
AWGN channels with unconstrained bandwidth. As a byproduct, we show that the
capacity with a message-informed helper that is available only at the
transmitter can exceed the capacity of the same scenario when the helper knows
only the noise but not the message.Comment: This work has been submitted to the IEEE for possible publication.
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